We have developed an on-line handwriting recognition system. Our approach
integrates local bottom-up constructs with a global top-down measure into a
modular recognition engine. The bottom-up process uses local point features
for hypothesizing character segmentations and the top-down part performs
shape matching for evaluating the segmentations. The shape comparison,
called Fisher segmental matching, is based on Fisher's linear discriminant
analysis. The component character recognizer of the system uses two kinds
of Fisher matching based on different representations and combines the
information to form the multiple experts paradigm.

Along with an efficient ligature modeling, the segmentations and their
character recognition scores are integrated into a recognition engine termed
Hypotheses Propagation Network (HPN), which runs a variant of topological
sort algorithm of graph search. The HPN improves on the conventional Hidden
Markov Model and the Viterbi search by using the more robust mean-based
scores for word level hypotheses and keeping multiple predecessors during
the search.

We have also studied and implemented a geometric context modeling termed
Visual Bigram Modeling that improves the accuracy of the system's
performance by taking the geometric constraints into account, in which the
component characters in a word can be formed in relation with the
neighboring characters. The result is a shape-oriented system, robust with
respect to local and temporal features, modular in construction and has a
rich range of opportunities for further extensions.